• Journal of the Korean Association of Geographic Information Studies
• /
• v.7 no.4
• /
• pp.34-45
• /
• 2004

Existing Semangeum's water balance analysis simplifies whole basin to single basin and achieved volume of effluence that produce by Kajiyama way to foundation. But Semangeum is complicated and various rice-wine strainer supply system. And there is difficulty to apply as elastic when water balance element is changed at free point. Divided to unit possession station for suitable water balance analysis model application to Semangeum in this study. And developed basin water balance model of GIS base that can do details analysis is bite about development and transfer of an appropriation in the budget of basin water resources. Achieved study including abstraction and concept design that use UML (unified modeling language) diagram for details analysis, stream network composition for rice-wine strainer supply system application, preprocessing of GIS base and postprocessing module development, model revision and verification etc. Support of this water balance analysis model is available to establish efficient water resources administration plan through outward flow process analysis of water resources. And support is considered to be possible in more convenient and, reasonable water resources administration way establishment by minimizing manual processing in systematic water resources government official to user and support diversified analysis system.

• Journal of Wetlands Research
• /
• v.26 no.2
• /
• pp.147-159
• /
• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.